Location: Children's Nutrition Research CenterTitle: Estimating circadian phase in elementary school children: Leveraging advances in physiologically-informed models of circadian entrainment and wearable devices
|MORENO, JENNETTE - Children'S Nutrition Research Center (CNRC)|
|HANNAY, KEVIN - University Of Michigan|
|WALCH, OLIVIA - University Of Michigan|
|DADABHOY, HAFZA - Children'S Nutrition Research Center (CNRC)|
|CHRISTIAN, JESSICA - Children'S Nutrition Research Center (CNRC)|
|EL-MUBASHER, ABEER - Children'S Nutrition Research Center (CNRC)|
|BACHA, FIDA - Children'S Nutrition Research Center (CNRC)|
|GRANT, SARAH - Children'S Nutrition Research Center (CNRC)|
|PARK, REBEKAH - Children'S Nutrition Research Center (CNRC)|
|CHENG, PHILIP - Henry Ford Hospital|
Submitted to: Sleep
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2022
Publication Date: 3/11/2022
Citation: Moreno, J.P., Hannay, K.M., Walch, O., Dadabhoy, H., Christian, J., El-Mubasher, A., Bacha, F., Grant, S.R., Park, R.J., Cheng, P. 2022. Estimating circadian phase in elementary school children: Leveraging advances in physiologically-informed models of circadian entrainment and wearable devices. Sleep. https://doi.org/10.1093/sleep/zsac061.
Interpretive Summary: Circadian phase refers to the timing of an individual's internal 24-hr biological clock. The ability to predict an individual's circadian phase has a variety of clinical implications such as optimal timing of vaccine or medication administration. However, the assessment of circadian phase requires the measurement of the time at which melatonin begins to rise and exceeds a pre-established threshold under dim light conditions (i.e., dim light melatonin onset). These procedures are costly and time-intensive for patients. Wearable devices and mathematical models of circadian physiology have shown promise to assist researchers in the accurate assessment of circadian phase in a cost-effective manner, yet these results have not been explored among children who are much more sensitive to light than adults. Sleep/wake patterns have been used to estimate dim light melatonin onset among adolescents aged 9-17. The current study extended these findings to elementary school children ages 5-8 years old to examine agreement between measured dim light melatonin onset and estimated dim light melatonin onset with regression equations using children's bedtime, sleep midpoint and waketime (i.e. sleep/wake behaviors). Estimates of dim light melatonin onset using sleep/wake behaviors were compared to estimates obtained using a physiological limit cycle oscillator model of circadian rhythms. Findings suggest that mathematical models of circadian physiology can facilitate more accurate predictions of children's circadian phase using data collected from wearable devices. Sleep/wake timing proved to be weak estimates of dim light melatonin onset in 5-8-year-old children.
Technical Abstract: The study objective was to examine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and waketime). As part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4+/-.97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lux). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared.DLMO predictions using the Hannay model outperformed DLMO predictions based on children's sleep/wake parameters with a Lin's Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41-0.59 for sleep/wake parameters. The mean absolute error was 31 minutes for the Hannay model compared to 35-38 minutes for the sleep/wake variables. Our findings suggest sleep-wake behaviors were weak proxies of DLMO phase in children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children.